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CPDZ: A Credibility-Aware and Privacy-Preserving Data Collection Scheme with Zero-Trust in Next-Generation Crowdsensing Networks

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2183-2199页

作者： Tang, Jianheng Fan, Kejia Yang, Shihao Liu, Anfeng Xiong, Neal N. Song, Houbing Herbert Leung, Victor C. M. Central South University School of Computer Science and Engineering Changsha410083 China Peking University School of Computer Science Beijing100871 China Sul Ross State University Department of Computer Mathematical and Physical Sciences AlpineTX79830 United States Department of Information Systems BaltimoreMD21250 United States Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Next-Generation Crowdsensing Networks (NGCNs) have become increasingly critical for smart cities, where data privacy and quality are pivotal concerns. Traditional trust mechanisms in crowdsensing mainly rely on static trust models, which are insufficient for dynamic security requirements. Zero-Trust security represents a promising opportunity, yet coming with notable challenges in NGCNs, including Unknown Workers Online Recruitment (UWOR), information Elicitation Without Verification (IEWV), Privacy Preserving Data Evaluation (PPDE), and Dynamic Trust Abrupt Shift (DTAS). To address these challenges, we propose a Credibility-aware and Privacy-preserving Data collection scheme with Zero-trust (CPDZ) for secure and quality data collection in NGCNs. First, our CPDZ scheme encompasses a quality worker recruitment strategy with combinatorial multi-armed bandit models, utilizing Thompson Sampling for the secure and efficient resolution of the UWOR. Second, an active dispatching scheme for unmanned aerial vehicles is crafted to collect data as a gold standard to assist in overcoming the IEWV challenge. Third, as for the PPDE challenge, we propose a lightweight privacy-preserving scheme for dependable truth discovery and secure trust verification. Fourth, the DTAS challenge is managed by a dual verification scheme that integrates short-term and long-term trust assessments, ensuring stability and adaptability of the zero-trust security in our CPDZ scheme. Experiments confirm the superiority of our CPDZ scheme, showing a 12.5% increase in recruitment revenue and a 57.8% reduction in relative error compared to existing approaches. © 1983-2012 IEEE.

关键词： Differential privacy

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Evaluation of the Effectiveness of information Technology Methods for Processing Diagnostic information Based on Complex Data 3

Evaluation of the Effectiveness of Information Technology Me...

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3rd IEEE International Conference on System Analysis and Intelligent Computing, SAIC 2022

作者： Ivanytska, Anastasiia Zubyk, Liudmyla Dudnik, Andriy Gololobov, Dmitry Kurchenko, Oleh Berestov, Denis Taras Shevchenko National University of Kyiv Department of Software Systems and Technologies Kyiv Ukraine Interregional Academy of Personnel Management Department of Computer Information Systems and Technologies Kyiv Ukraine National Aviation University Department of Software Engineering Kyiv Ukraine

ISBN: (数字)9798350396744

ISBN: (纸本)9798350396744

The developed models were tested for adequacy and the effectiveness of information technology methods for processing diagnostic information based on complex data was evaluated. The obtained indicators of informativeness of diagnostic tools showed high values of sensitivity, specificity and accuracy, the latest coincides with the actual one by no less than 94.39. A control study of the effectiveness of the methods showed that it was possible to reduce the time spent on diagnosis by at least 8.97%. © 2022 IEEE.

关键词： Diagnosis

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Iot device identification using deep learning 13th

Iot device identification using deep learning

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13th International Conference on Computational Intelligence in Security for information systems, CISIS 2020

作者： Kotak, Jaidip Elovici, Yuval Department of Software and Information Systems Engineering Ben-Gurion University Beer-Sheva Israel

ISBN: (纸本)9783030578046

The growing use of IoT devices in organizations has increased the number of attack vectors available to attackers due to the less secure nature of the devices. The widely adopted bring your own device (BYOD) policy which allows an employee to bring any IoT device into the workplace and attach it to an organization’s network also increases the risk of attacks. In order to address this threat, organizations often implement security policies in which only the connection of white-listed IoT devices is permitted. To monitor adherence to such policies and protect their networks, organizations must be able to identify the IoT devices connected to their networks and, more specifically, to identify connected IoT devices that are not on the white-list (unknown devices). In this study, we applied deep learning on network traffic to automatically identify IoT devices connected to the network. In contrast to previous work, our approach does not require that complex feature engineering be applied on the network traffic, since we represent the "communication behavior" of IoT devices using small images built from the IoT devices’ network traffic payloads. In our experiments, we trained a multiclass classifier on a publicly available dataset, successfully identifying 10 different IoT devices and the traffic of smartphones and computers, with over 99% accuracy. We also trained multiclass classifiers to detect unauthorized IoT devices connected to the network, achieving over 99% overall average detection accuracy. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Internet of things

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Keeping Behavioral Programs Alive: Specifying and Executing Liveness Requirements

arXiv

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arXiv 2024年

作者： Yaacov, Tom Elyasaf, Achiya Weiss, Gera Computer Science Department Ben-Gurion University of the Negev Software and Information Systems Engineering Department Ben-Gurion University of the Negev

One of the benefits of using executable specifications such as Behavioral Programming (BP) is the ability to align the system implementation with its requirements. This is facilitated in BP by a protocol that allows independent implementation modules that specify what the system may, must, and must not do. By that, each module can enforce a single system requirement, including negative specifications such as "don’t do X after Y." The existing BP protocol, however, allows only the enforcement of safety requirements and does not support the execution of liveness properties such as "do X at least three times." To model liveness requirements in BP directly and independently, we propose idioms for tagging states with "must-finish," indicating that tasks are yet to be completed. We show that this idiom allows a direct specification of known requirements patterns from the literature. We also offer semantics and two execution mechanisms, one based on a translation to Büchi automata and the other based on a Markov decision process (MDP). The latter approach offers the possibility of utilizing deep reinforcement learning (DRL) algorithms, which bear the potential to handle large software systems effectively. This paper presents a qualitative and quantitative assessment of the proposed approach using a proof-of-concept tool. A formal analysis of the MDP-based execution mechanism is given in an appendix. © 2024, CC BY.

关键词： Markov processes

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Robust Counting in Overcrowded Scenes Using Batch-Free Normalized Deep ConvNet

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Computer systems Science & engineering 2023年第9期46卷 2741-2754页

作者： Sana Zahir Rafi Ullah Khan Mohib Ullah Muhammad Ishaq Naqqash Dilshad Amin Ullah Mi Young Lee Institute of Computer Sciences and Information Technology(ICS/IT) The University of AgriculturePeshawar25130Khyber PakhtunkhwaPakistan Department of Convergence Engineering for Intelligent Drone Sejong UniversitySeoul05006Korea Collaborative Robotics and Intelligent Systems(CoRIS)Institute Oregon State UniversityORUSA Department of Software Sejong UniversitySeoul05006Korea

The analysis of overcrowded areas is essential for flow monitoring,assembly control,and *** counting’s primary goal is to calculate the population in a given region,which requires real-time analysis of congested scenes for prompt reactionary *** crowd is always unexpected,and the benchmarked available datasets have a lot of variation,which limits the trained models’performance on unseen test *** this paper,we proposed an end-to-end deep neural network that takes an input image and generates a density map of a crowd *** proposed model consists of encoder and decoder networks comprising batch-free normalization layers known as evolving normalization(EvoNorm).This allows our network to be generalized for unseen data because EvoNorm is not using statistics from the training *** decoder network uses dilated 2D convolutional layers to provide large receptive fields and fewer parameters,which enables real-time processing and solves the density drift problem due to its large receptive *** benchmark datasets are used in this study to assess the proposed model,resulting in the conclusion that it outperforms conventional models.

关键词： Artificial intelligence deep learning crowd counting scene understanding

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Transformer-based Drum-level Prediction in a Boiler Plant with Delayed Relations among Multivariates

Transformer-based Drum-level Prediction in a Boiler Plant wi...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Gang Su Sun Yang Zhishuai Li Ziyue Li SenseTime Research China School of Software Engineering Peking University Beijing China Information Systems University of Cologne Cologne Germany

ISBN: (数字)9798350390421

ISBN: (纸本)9798350390438

The steam drum water level is a critical parameter that directly impacts the safety and efficiency of power plant operations. However, predicting the drum water level in boilers is challenging due to complex non-linear process dynamics originating from long-time delays and interrelations, as well as measurement noise. This paper investigates the application of Transformer-based models for predicting drum water levels in a steam boiler plant. Leveraging the capabilities of Transformer architectures, this study aims to develop an accurate and robust predictive framework to anticipate water level fluctuations and facilitate proactive control strategies. To this end, a prudent pipeline is proposed, including 1) data preprocess, 2) causal relation analysis, 3) delay inference, 4) variable augmentation, and 5) prediction. Through extensive experimentation and analysis, the effectiveness of Transformer-based approaches in steam drum water level prediction is evaluated, highlighting their potential to enhance operational stability and optimize plant performance.

关键词： Accuracy Europe Predictive models Transformers Boilers Solids Stability analysis Delays Smart grids Safety

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ECG Signals Classification Method for Wireless Body Area Network Based on Quantized Residual Network 22

ECG Signals Classification Method for Wireless Body Area Net...

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5th International Conference on Big Data Technologies, ICBDT 2022

作者： Peng, Xiangdong Xiong, Panxia Song, William Wei Zhou, Xiao Zhu, Huaqiang Zhang, Quan Lu, Yong Zheng, Tengfei School of Software and Internet of Things Engineering Jiangxi University of Finance and Economics Nanchang330013 China Department of Information Systems Dalarna University Falun79188 Sweden

ISBN: (纸本)9781450396875

In recent years, rapidly developing deep neural network methods have been widely used for ECG signals classification. However, deep neural networks generally have problems such as too many network redundant parameters, high computational complexity, and large model storage, which are not suitable for application in resource-constrained wireless body area network (WBAN). To address the above problems, this paper proposes a quantized residual network-based ECG signals classification method for WBAN. The method firstly proposes a full-precision residual network, which is activated by adding the input and output of the residual unit through the form of a hopping layer connection and using a one-dimensional convolutional layer as the residual information to enhance the characteristics of the residual block. The test of the MIT-BIH arrhythmia database shows that the accuracy of ECG classification is 99.18%. Secondly, the parameters of the above full-precision residual network are fixed-point binary quantized, and the same database test shows that the final model memory occupation is compressed by 32 times, and the classification accuracy is 99.07%. The proposed method effectively solves the problems of many redundant parameters and large model storage in deep neural networks while ensuring high classification accuracy, and provides some reference for the research of low power consumption in WBAN. © 2022 ACM.

关键词： Deep neural networks

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VisioSLR: A Vision Data-Driven Framework for Sign Language Video Recognition and Performance Evaluation on Fine-Tuned YOLO Models

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Procedia Computer Science 2025年 257卷 85-92页

作者： Leila Ismail Nada Shahin Henok Tesfaye Alain Hennebelle Intelligent Distributed Computing and Systems (INDUCE) Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates Independent Researcher

With the emergence of AI for good, there has been an increasing interest in building computer vision data-driven deep learning inclusive AI solutions. Sign language Recognition (SLR) has gained attention recently. It is an essential component of a sign-to-text translation system to support the deaf and hard-of-hearing population. This paper presents a computer VISIOn data-driven deep learning framework for Sign Language video Recognition (VisoSLR). VisioSLR provides a precise measurement of translating signs for developing an end-to-end computational translation system. Considering the scarcity of sign language datasets, which hinders the development of an accurate recognition model, we evaluate the performance of our framework by fine-tuning the very well-known YOLO models, which are built from a signs-unrelated collection of images and videos, using a small-sized sign language dataset. Gathering a sign language dataset for signs training would involve an enormous amount of time to collect and annotate videos in different environmental setups and multiple signers, in addition to the training time of a model. Numerical evaluations of VisioSLR show that our framework recognizes signs with a mean average precision of 97.4%, 97.1%, and 95.5% and 11, 12, and 12 milliseconds of recognition time on YOLOv8m, YOLOv9m, and YOLOv11m, respectively.

关键词： Artificial intelligence Computer vision Deep learning Fine-tuning Machine translation Neural machine translation Sign language Sign language recognition Transfer learning YOLO

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Mind The Gap: Can Air-Gaps Keep Your Private Data Secure?

arXiv

引用

arXiv 2024年

作者： Guri, Mordechai Ben-Gurion University of the Negev Israel Department of Software and Information Systems Engineering Air-Gap Research Lab United States

Personal data has become one of the most valuable assets and lucrative targets for attackers in the modern digital world. This includes personal identification information (PII), medical records, legal information, biometric data, and private communications. To protect it from hackers, 'air-gap' measures might be employed. This protective strategy keeps sensitive data in networks entirely isolated (physically and logically) from the Internet. Creating a physical 'air gap' between internal networks and the outside world safeguards sensitive data from theft and online threats. Air-gap networks are relevant today to governmental organizations, healthcare industries, finance sectors, intellectual property and legal firms, and others. In this paper, we dive deep into air-gap security in light of modern cyberattacks and data privacy. Despite this level of protection, publicized incidents from the last decade show that even air-gap networks are not immune to breaches. Motivated and capable adversaries can use sophisticated attack vectors to penetrate the air-gapped networks, leaking sensitive data *** focus on different aspects of air gap security. First, we overview cyber incidents that target air-gap networks, including infamous ones such as ***. Second, we introduce the adversarial attack model and different attack vectors attackers may use to compromise air-gap networks. Third, we present the techniques attackers can apply to leak data out of air-gap networks and introduce more innovative ones based on our recent research. We demonstrate that despite the disconnection from the Internet, attackers can exploit special types of covert channels to exfiltrate confidential data. We present the different types of covert channels, including electromagnetic, electrical, optical, thermal, and acoustic techniques. We show how attackers can use them stealthily. Our paper shows how advanced persistent threats (APTs) can leak private and confidential information using t

关键词： information leakage

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Keeping Behavioral Programs Alive: Specifying and Executing Liveness Requirements

Keeping Behavioral Programs Alive: Specifying and Executing ...

引用

IEEE International Conference on Requirements engineering

作者： Tom Yaacov Achiya Elyasaf Gera Weiss Computer Science Department Ben-Gurion University of the Negev Software and Information Systems Engineering Department Ben-Gurion University of the Negev

ISBN: (数字)9798350395112

ISBN: (纸本)9798350395129

One of the benefits of using executable specifications such as Behavioral Programming (BP) is the ability to align the system implementation with its requirements. This is facilitated in BP by a protocol that allows independent implementation modules that specify what the system may, must, and must not do. By that, each module can enforce a single system requirement, including negative specifications such as “don't do X after Y.” The existing BP protocol, however, allows only the enforcement of safety requirements and does not support the execution of liveness properties such as “do X at least three times.” To model liveness requirements in BP directly and independently, we propose idioms for tagging states with “must-finish;’ indicating that tasks are yet to be completed. We show that this idiom allows a direct specification of known requirements patterns from the literature. We also offer semantics and two execution mechanisms, one based on a translation to Büchi automata and the other based on a Markov decision process (MDP). The latter approach offers the possibility of utilizing deep reinforcement learning (DRL) algorithms, which bear the potential to handle large software systems effectively. This paper presents a qualitative and quantitative assessment of the proposed approach using a proof-of-concept tool. A formal analysis of the MDP-based execution mechanism is given in an appendix.

关键词： Protocols software algorithms Semantics Tagging Programming software systems Safety

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